https://doi.org/10.5281/zenodo.3635430
Histopathology Research Template 🔬
Describe Materials and Methods as highlighted in (Knijn, Simmer, and Nagtegaal 2015).2
Describe patient characteristics, and inclusion and exclusion criteria
Describe treatment details
Describe the type of material used
Specify how expression of the biomarker was assessed
Describe the number of independent (blinded) scorers and how they scored
State the method of case selection, study design, origin of the cases, and time frame
Describe the end of the follow-up period and median follow-up time
Define all clinical endpoints examined
Specify all applied statistical methods
Describe how interactions with other clinical/pathological factors were analyzed
Codes for general settings.3
Setup global chunk settings4
knitr::opts_chunk$set(
eval = TRUE,
echo = TRUE,
fig.path = here::here("figs/"),
message = FALSE,
warning = FALSE,
error = FALSE,
cache = TRUE,
comment = NA,
tidy = TRUE,
fig.width = 6,
fig.height = 4
)Load Library
see R/loadLibrary.R for the libraries loaded.
Codes for generating fake data.5
Generate Fake Data
This code generates a fake histopathological data. Some sources for fake data generation here6 , here7 , here8 , here9 , here10 , here11 , here12 , here13 , and here14 .
Use this code to generate fake clinicopathologic data
Codes for importing data.15
Read the data
library(readxl)
mydata <- readxl::read_excel(here::here("data", "mydata.xlsx"))
# View(mydata) # Use to view data after importingAdd code for import multiple data purrr reduce
Codes for reporting general features.16
Dataframe Report
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others (0 missing)
- Name: 249 entries: Aaleyah, n = 1; Abrea, n = 1; Afonso, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 127; Female, n = 122 (1 missing)
- Age: Mean = 48.59, SD = 14.12, Median = , MAD = 17.79, range: [25, 73], Skewness = 0.07, Kurtosis = -1.17, 1 missing
- Race: 7 entries: White, n = 151; Hispanic, n = 49; Black, n = 30 and 4 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 205; Present, n = 44 (1 missing)
- LVI: 2 entries: Absent, n = 148; Present, n = 102 (0 missing)
- PNI: 2 entries: Absent, n = 181; Present, n = 68 (1 missing)
- Death: 2 levels: FALSE (n = 80, 32.00%); TRUE (n = 169, 67.60%) and missing (n = 1, 0.40%)
- Group: 2 entries: Treatment, n = 130; Control, n = 119 (1 missing)
- Grade: 3 entries: 3, n = 97; 2, n = 84; 1, n = 68 (1 missing)
- TStage: 4 entries: 4, n = 113; 3, n = 65; 2, n = 49 and 1 other (0 missing)
- AntiX_intensity: Mean = 2.39, SD = 0.66, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.62, Kurtosis = -0.66, 1 missing
- AntiY_intensity: Mean = 1.91, SD = 0.75, Median = , MAD = 1.48, range: [1, 3], Skewness = 0.15, Kurtosis = -1.20, 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 147; Present, n = 102 (1 missing)
- Valid: 2 levels: FALSE (n = 134, 53.60%); TRUE (n = 115, 46.00%) and missing (n = 1, 0.40%)
- Smoker: 2 levels: FALSE (n = 132, 52.80%); TRUE (n = 117, 46.80%) and missing (n = 1, 0.40%)
- Grade_Level: 3 entries: high, n = 91; moderate, n = 83; low, n = 75 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101 (0 missing)
250 observations with 21 variables
17 variables containing missings (NA)
0 variables with no variance
Codes for defining variable types.19
print column names as vector
c("ID", "Name", "Sex", "Age", "Race", "PreinvasiveComponent",
"LVI", "PNI", "LastFollowUpDate", "Death", "Group", "Grade",
"TStage", "AntiX_intensity", "AntiY_intensity", "LymphNodeMetastasis",
"Valid", "Smoker", "Grade_Level", "SurgeryDate", "DeathTime")
vctrs::vec_assert()
dplyr::all_equal()
arsenal::compare()
visdat::vis_compare()
See the code as function in R/find_key.R.
keycolumns <- mydata %>% sapply(., FUN = dataMaid::isKey) %>% tibble::as_tibble() %>%
dplyr::select(which(.[1, ] == TRUE)) %>% names()
keycolumns[1] "ID" "Name"
Get variable types
# A tibble: 4 x 4
type cnt pcnt col_name
<chr> <int> <dbl> <list>
1 character 11 57.9 <chr [11]>
2 logical 3 15.8 <chr [3]>
3 numeric 3 15.8 <chr [3]>
4 POSIXct POSIXt 2 10.5 <chr [2]>
mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>% describer::describe() %>%
knitr::kable(format = "markdown")| .column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
|---|---|---|---|---|---|---|---|---|---|---|
| Sex | character | character | 250 | NA | NA | Female | NA | NA | NA | Male |
| Age | numeric | double | 250 | 48.586345 | 14.1156504 | 25 | 36 | 48 | 61 | 73 |
| Race | character | character | 250 | NA | NA | Asian | NA | NA | NA | White |
| PreinvasiveComponent | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| LVI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| PNI | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Death | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Group | character | character | 250 | NA | NA | Control | NA | NA | NA | Treatment |
| Grade | character | character | 250 | NA | NA | 1 | NA | NA | NA | 3 |
| TStage | character | character | 250 | NA | NA | 1 | NA | NA | NA | 4 |
| AntiX_intensity | numeric | double | 250 | 2.385542 | 0.6629235 | 1 | 2 | 2 | 3 | 3 |
| AntiY_intensity | numeric | double | 250 | 1.907631 | 0.7483194 | 1 | 1 | 2 | 2 | 3 |
| LymphNodeMetastasis | character | character | 250 | NA | NA | Absent | NA | NA | NA | Present |
| Valid | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Smoker | logical | logical | 250 | NA | NA | FALSE | NA | NA | NA | TRUE |
| Grade_Level | character | character | 250 | NA | NA | high | NA | NA | NA | moderate |
| DeathTime | character | character | 250 | NA | NA | MoreThan1Year | NA | NA | NA | Within1Year |
Plot variable types
# https://github.com/ropensci/visdat
# http://visdat.njtierney.com/articles/using_visdat.html
# https://cran.r-project.org/web/packages/visdat/index.html
# http://visdat.njtierney.com/
# visdat::vis_guess(mydata)
visdat::vis_dat(mydata)character variablescharacterVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "character") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
characterVariables [1] "Sex" "Race" "PreinvasiveComponent"
[4] "LVI" "PNI" "Group"
[7] "Grade" "TStage" "LymphNodeMetastasis"
[10] "Grade_Level" "DeathTime"
categorical variablescategoricalVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"factor") %>% dplyr::select(column_name) %>% dplyr::pull()
categoricalVariablescharacter(0)
continious variablescontiniousVariables <- mydata %>% dplyr::select(-keycolumns, -contains("Date")) %>%
describer::describe() %>% janitor::clean_names() %>% dplyr::filter(column_type ==
"numeric" | column_type == "double") %>% dplyr::select(column_name) %>% dplyr::pull()
continiousVariables[1] "Age" "AntiX_intensity" "AntiY_intensity"
numeric variablesnumericVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "numeric") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
numericVariables[1] "Age" "AntiX_intensity" "AntiY_intensity"
integer variablesintegerVariables <- mydata %>% dplyr::select(-keycolumns) %>% inspectdf::inspect_types() %>%
dplyr::filter(type == "integer") %>% dplyr::select(col_name) %>% dplyr::pull() %>%
unlist()
integerVariablesNULL
Codes for overviewing the data.20
reactable::reactable(data = mydata, sortable = TRUE, resizable = TRUE, filterable = TRUE,
searchable = TRUE, pagination = TRUE, paginationType = "numbers", showPageSizeOptions = TRUE,
highlight = TRUE, striped = TRUE, outlined = TRUE, compact = TRUE, wrap = FALSE,
showSortIcon = TRUE, showSortable = TRUE)Summary of Data via summarytools 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
summarytools::view(x = summarytools::dfSummary(mydata %>% dplyr::select(-keycolumns)),
file = here::here("out", "mydata_summary.html"))Summary via dataMaid 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
dataMaid::makeDataReport(data = mydata, file = here::here("out", "dataMaid_mydata.Rmd"),
replace = TRUE, openResult = FALSE, render = FALSE, quiet = TRUE)Summary via explore 📦
if (!dir.exists(here::here("out"))) {
dir.create(here::here("out"))
}
mydata %>% dplyr::select(-dateVariables) %>% explore::report(output_file = "mydata_report.html",
output_dir = here::here("out"))Glimpse of Data
Observations: 250
Variables: 17
$ Sex <chr> "Female", "Female", "Female", "Female", "Male", …
$ Age <dbl> 54, 39, 32, 58, 38, 50, 36, 70, 43, 45, 65, 27, …
$ Race <chr> "White", "White", "White", "Hispanic", "White", …
$ PreinvasiveComponent <chr> "Present", "Absent", "Absent", "Present", "Prese…
$ LVI <chr> "Present", "Absent", "Present", "Absent", "Absen…
$ PNI <chr> "Absent", "Absent", "Absent", "Present", "Absent…
$ Death <lgl> TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRU…
$ Group <chr> "Treatment", "Treatment", "Control", "Treatment"…
$ Grade <chr> "1", "3", "3", "3", "2", "1", "2", "2", "1", "3"…
$ TStage <chr> "4", "4", "2", "1", "1", "4", "4", "4", "4", "1"…
$ AntiX_intensity <dbl> 2, 3, 3, 3, 2, 2, 1, 3, 2, 2, 3, 2, 3, 1, 3, 3, …
$ AntiY_intensity <dbl> 1, 1, 1, 1, 3, 3, 2, 3, 2, 2, 2, 1, 1, 2, 2, 2, …
$ LymphNodeMetastasis <chr> "Present", "Absent", "Absent", "Absent", "Presen…
$ Valid <lgl> FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, F…
$ Smoker <lgl> FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TR…
$ Grade_Level <chr> "low", "low", "moderate", "high", "high", "moder…
$ DeathTime <chr> "Within1Year", "Within1Year", "Within1Year", "Wi…
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 48.6 73
5 Race chr 1 0.4 8 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 0 0 2 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.68 1
# … with 11 more rows
Explore
Control Data if matching expectations
visdat::vis_expect(data = mydata, expectation = ~.x == -1, show_perc = TRUE)
visdat::vis_expect(mydata, ~.x >= 25)See missing values
$variables
Variable q qNA pNA qZero pZero qBlank pBlank qInf pInf
1 Valid 250 1 0.4% 134 53.6% 0 - 0 -
2 Smoker 250 1 0.4% 132 52.8% 0 - 0 -
3 Death 250 1 0.4% 80 32% 0 - 0 -
4 Sex 250 1 0.4% 0 - 0 - 0 -
5 PreinvasiveComponent 250 1 0.4% 0 - 0 - 0 -
6 PNI 250 1 0.4% 0 - 0 - 0 -
7 Group 250 1 0.4% 0 - 0 - 0 -
8 LymphNodeMetastasis 250 1 0.4% 0 - 0 - 0 -
9 Grade 250 1 0.4% 0 - 0 - 0 -
10 AntiX_intensity 250 1 0.4% 0 - 0 - 0 -
11 AntiY_intensity 250 1 0.4% 0 - 0 - 0 -
12 Grade_Level 250 1 0.4% 0 - 0 - 0 -
13 Race 250 1 0.4% 0 - 0 - 0 -
14 LastFollowUpDate 250 1 0.4% 0 - 0 - 0 -
15 Age 250 1 0.4% 0 - 0 - 0 -
16 SurgeryDate 250 1 0.4% 0 - 0 - 0 -
17 Name 250 1 0.4% 0 - 0 - 0 -
18 LVI 250 0 - 0 - 0 - 0 -
19 DeathTime 250 0 - 0 - 0 - 0 -
20 TStage 250 0 - 0 - 0 - 0 -
21 ID 250 0 - 0 - 0 - 0 -
qDistinct type anomalous_percent
1 3 Logical 54%
2 3 Logical 53.2%
3 3 Logical 32.4%
4 3 Character 0.4%
5 3 Character 0.4%
6 3 Character 0.4%
7 3 Character 0.4%
8 3 Character 0.4%
9 4 Character 0.4%
10 4 Numeric 0.4%
11 4 Numeric 0.4%
12 4 Character 0.4%
13 8 Character 0.4%
14 13 Timestamp 0.4%
15 50 Numeric 0.4%
16 229 Timestamp 0.4%
17 250 Character 0.4%
18 2 Character -
19 2 Character -
20 4 Character -
21 250 Character -
$problem_variables
[1] Variable q qNA pNA
[5] qZero pZero qBlank pBlank
[9] qInf pInf qDistinct type
[13] anomalous_percent problems
<0 rows> (or 0-length row.names)
================================================================================
[1] "Ignoring variable LastFollowUpDate: Unsupported type for visualization."
[1] "Ignoring variable SurgeryDate: Unsupported type for visualization."
Variable p_1 p_10 p_25 p_50 p_75 p_90 p_99
1 AntiX_intensity 1 1.8 2 2 3 3 3
2 AntiY_intensity 1 1 1 2 2 3 3
3 Age 25 29.8 36 48 61 68.2 73
Summary of Data via DataExplorer 📦
# A tibble: 1 x 9
rows columns discrete_columns continuous_colu… all_missing_col…
<int> <int> <int> <int> <int>
1 250 21 18 3 0
# … with 4 more variables: total_missing_values <int>, complete_rows <int>,
# total_observations <int>, memory_usage <dbl>
Drop columns
Write results as described in (Knijn, Simmer, and Nagtegaal 2015)22
Describe the number of patients included in the analysis and reason for dropout
Report patient/disease characteristics (including the biomarker of interest) with the number of missing values
Describe the interaction of the biomarker of interest with established prognostic variables
Include at least 90 % of initial cases included in univariate and multivariate analyses
Report the estimated effect (relative risk/odds ratio, confidence interval, and p value) in univariate analysis
Report the estimated effect (hazard rate/odds ratio, confidence interval, and p value) in multivariate analysis
Report the estimated effects (hazard ratio/odds ratio, confidence interval, and p value) of other prognostic factors included in multivariate analysis
Codes for Descriptive Statistics.23
Report Data properties via report 📦
The data contains 250 observations of the following variables:
- ID: 250 entries: 001, n = 1; 002, n = 1; 003, n = 1 and 247 others (0 missing)
- Name: 249 entries: Aaleyah, n = 1; Abrea, n = 1; Afonso, n = 1 and 246 others (1 missing)
- Sex: 2 entries: Male, n = 127; Female, n = 122 (1 missing)
- Age: Mean = 48.59, SD = 14.12, Median = , MAD = 17.79, range: [25, 73], Skewness = 0.07, Kurtosis = -1.17, 1 missing
- Race: 7 entries: White, n = 151; Hispanic, n = 49; Black, n = 30 and 4 others (1 missing)
- PreinvasiveComponent: 2 entries: Absent, n = 205; Present, n = 44 (1 missing)
- LVI: 2 entries: Absent, n = 148; Present, n = 102 (0 missing)
- PNI: 2 entries: Absent, n = 181; Present, n = 68 (1 missing)
- Death: 2 levels: FALSE (n = 80, 32.00%); TRUE (n = 169, 67.60%) and missing (n = 1, 0.40%)
- Group: 2 entries: Treatment, n = 130; Control, n = 119 (1 missing)
- Grade: 3 entries: 3, n = 97; 2, n = 84; 1, n = 68 (1 missing)
- TStage: 4 entries: 4, n = 113; 3, n = 65; 2, n = 49 and 1 other (0 missing)
- AntiX_intensity: Mean = 2.39, SD = 0.66, Median = , MAD = 1.48, range: [1, 3], Skewness = -0.62, Kurtosis = -0.66, 1 missing
- AntiY_intensity: Mean = 1.91, SD = 0.75, Median = , MAD = 1.48, range: [1, 3], Skewness = 0.15, Kurtosis = -1.20, 1 missing
- LymphNodeMetastasis: 2 entries: Absent, n = 147; Present, n = 102 (1 missing)
- Valid: 2 levels: FALSE (n = 134, 53.60%); TRUE (n = 115, 46.00%) and missing (n = 1, 0.40%)
- Smoker: 2 levels: FALSE (n = 132, 52.80%); TRUE (n = 117, 46.80%) and missing (n = 1, 0.40%)
- Grade_Level: 3 entries: high, n = 91; moderate, n = 83; low, n = 75 (1 missing)
- DeathTime: 2 entries: Within1Year, n = 149; MoreThan1Year, n = 101 (0 missing)
Table 1 via arsenal 📦
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- arsenal::tableby(
~ Sex +
Age +
Race +
PreinvasiveComponent +
LVI +
PNI +
Death +
Group +
Grade +
TStage +
# `Anti-X-intensity` +
# `Anti-Y-intensity` +
LymphNodeMetastasis +
Valid +
Smoker +
Grade_Level
,
data = mydata
)
summary(tab1)| Overall (N=250) | |
|---|---|
| Sex | |
| N-Miss | 1 |
| Female | 122 (49.0%) |
| Male | 127 (51.0%) |
| Age | |
| N-Miss | 1 |
| Mean (SD) | 48.586 (14.116) |
| Range | 25.000 - 73.000 |
| Race | |
| N-Miss | 1 |
| Asian | 11 (4.4%) |
| Bi-Racial | 5 (2.0%) |
| Black | 30 (12.0%) |
| Hispanic | 49 (19.7%) |
| Native | 1 (0.4%) |
| Other | 2 (0.8%) |
| White | 151 (60.6%) |
| PreinvasiveComponent | |
| N-Miss | 1 |
| Absent | 205 (82.3%) |
| Present | 44 (17.7%) |
| LVI | |
| Absent | 148 (59.2%) |
| Present | 102 (40.8%) |
| PNI | |
| N-Miss | 1 |
| Absent | 181 (72.7%) |
| Present | 68 (27.3%) |
| Death | |
| N-Miss | 1 |
| FALSE | 80 (32.1%) |
| TRUE | 169 (67.9%) |
| Group | |
| N-Miss | 1 |
| Control | 119 (47.8%) |
| Treatment | 130 (52.2%) |
| Grade | |
| N-Miss | 1 |
| 1 | 68 (27.3%) |
| 2 | 84 (33.7%) |
| 3 | 97 (39.0%) |
| TStage | |
| 1 | 23 (9.2%) |
| 2 | 49 (19.6%) |
| 3 | 65 (26.0%) |
| 4 | 113 (45.2%) |
| LymphNodeMetastasis | |
| N-Miss | 1 |
| Absent | 147 (59.0%) |
| Present | 102 (41.0%) |
| Valid | |
| N-Miss | 1 |
| FALSE | 134 (53.8%) |
| TRUE | 115 (46.2%) |
| Smoker | |
| N-Miss | 1 |
| FALSE | 132 (53.0%) |
| TRUE | 117 (47.0%) |
| Grade_Level | |
| N-Miss | 1 |
| high | 91 (36.5%) |
| low | 75 (30.1%) |
| moderate | 83 (33.3%) |
Table 1 via tableone 📦
library(tableone)
mydata %>% dplyr::select(-keycolumns, -dateVariables) %>% tableone::CreateTableOne(data = .)
Overall
n 250
Sex = Male (%) 127 (51.0)
Age (mean (SD)) 48.59 (14.12)
Race (%)
Asian 11 ( 4.4)
Bi-Racial 5 ( 2.0)
Black 30 (12.0)
Hispanic 49 (19.7)
Native 1 ( 0.4)
Other 2 ( 0.8)
White 151 (60.6)
PreinvasiveComponent = Present (%) 44 (17.7)
LVI = Present (%) 102 (40.8)
PNI = Present (%) 68 (27.3)
Death = TRUE (%) 169 (67.9)
Group = Treatment (%) 130 (52.2)
Grade (%)
1 68 (27.3)
2 84 (33.7)
3 97 (39.0)
TStage (%)
1 23 ( 9.2)
2 49 (19.6)
3 65 (26.0)
4 113 (45.2)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 1.91 (0.75)
LymphNodeMetastasis = Present (%) 102 (41.0)
Valid = TRUE (%) 115 (46.2)
Smoker = TRUE (%) 117 (47.0)
Grade_Level (%)
high 91 (36.5)
low 75 (30.1)
moderate 83 (33.3)
DeathTime = Within1Year (%) 149 (59.6)
Descriptive Statistics of Continuous Variables
mydata %>% dplyr::select(continiousVariables, numericVariables, integerVariables) %>%
summarytools::descr(., style = "rmarkdown")# A tibble: 15 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Sex chr 1 0.4 3 NA NA NA
2 PreinvasiveComponent chr 1 0.4 3 NA NA NA
3 LVI chr 0 0 2 NA NA NA
4 PNI chr 1 0.4 3 NA NA NA
5 Death lgl 1 0.4 3 0 0.68 1
6 Group chr 1 0.4 3 NA NA NA
7 Grade chr 1 0.4 4 NA NA NA
8 TStage chr 0 0 4 NA NA NA
9 AntiX_intensity dbl 1 0.4 4 1 2.39 3
10 AntiY_intensity dbl 1 0.4 4 1 1.91 3
11 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
12 Valid lgl 1 0.4 3 0 0.46 1
13 Smoker lgl 1 0.4 3 0 0.47 1
14 Grade_Level chr 1 0.4 4 NA NA NA
15 DeathTime chr 0 0 2 NA NA NA
# A tibble: 17 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 Name chr 1 0.4 250 NA NA NA
2 Sex chr 1 0.4 3 NA NA NA
3 Age dbl 1 0.4 50 25 48.6 73
4 Race chr 1 0.4 8 NA NA NA
5 PreinvasiveComponent chr 1 0.4 3 NA NA NA
6 PNI chr 1 0.4 3 NA NA NA
7 LastFollowUpDate dat 1 0.4 13 NA NA NA
8 Death lgl 1 0.4 3 0 0.68 1
9 Group chr 1 0.4 3 NA NA NA
10 Grade chr 1 0.4 4 NA NA NA
11 AntiX_intensity dbl 1 0.4 4 1 2.39 3
12 AntiY_intensity dbl 1 0.4 4 1 1.91 3
13 LymphNodeMetastasis chr 1 0.4 3 NA NA NA
14 Valid lgl 1 0.4 3 0 0.46 1
15 Smoker lgl 1 0.4 3 0 0.47 1
16 Grade_Level chr 1 0.4 4 NA NA NA
17 SurgeryDate dat 1 0.4 229 NA NA NA
# A tibble: 21 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 ID chr 0 0 250 NA NA NA
2 Name chr 1 0.4 250 NA NA NA
3 Sex chr 1 0.4 3 NA NA NA
4 Age dbl 1 0.4 50 25 48.6 73
5 Race chr 1 0.4 8 NA NA NA
6 PreinvasiveComponent chr 1 0.4 3 NA NA NA
7 LVI chr 0 0 2 NA NA NA
8 PNI chr 1 0.4 3 NA NA NA
9 LastFollowUpDate dat 1 0.4 13 NA NA NA
10 Death lgl 1 0.4 3 0 0.68 1
# … with 11 more rows
Use R/gc_desc_cat.R to generate gc_desc_cat.Rmd containing descriptive statistics for categorical variables
mydata %>% janitor::tabyl(Sex) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Sex | n | percent | valid_percent |
|---|---|---|---|
| Female | 122 | 48.8% | 49.0% |
| Male | 127 | 50.8% | 51.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Race) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Race | n | percent | valid_percent |
|---|---|---|---|
| Asian | 11 | 4.4% | 4.4% |
| Bi-Racial | 5 | 2.0% | 2.0% |
| Black | 30 | 12.0% | 12.0% |
| Hispanic | 49 | 19.6% | 19.7% |
| Native | 1 | 0.4% | 0.4% |
| Other | 2 | 0.8% | 0.8% |
| White | 151 | 60.4% | 60.6% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(PreinvasiveComponent) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PreinvasiveComponent | n | percent | valid_percent |
|---|---|---|---|
| Absent | 205 | 82.0% | 82.3% |
| Present | 44 | 17.6% | 17.7% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(LVI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LVI | n | percent |
|---|---|---|
| Absent | 148 | 59.2% |
| Present | 102 | 40.8% |
mydata %>% janitor::tabyl(PNI) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| PNI | n | percent | valid_percent |
|---|---|---|---|
| Absent | 181 | 72.4% | 72.7% |
| Present | 68 | 27.2% | 27.3% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Group) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Group | n | percent | valid_percent |
|---|---|---|---|
| Control | 119 | 47.6% | 47.8% |
| Treatment | 130 | 52.0% | 52.2% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade | n | percent | valid_percent |
|---|---|---|---|
| 1 | 68 | 27.2% | 27.3% |
| 2 | 84 | 33.6% | 33.7% |
| 3 | 97 | 38.8% | 39.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(TStage) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| TStage | n | percent |
|---|---|---|
| 1 | 23 | 9.2% |
| 2 | 49 | 19.6% |
| 3 | 65 | 26.0% |
| 4 | 113 | 45.2% |
mydata %>% janitor::tabyl(LymphNodeMetastasis) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| LymphNodeMetastasis | n | percent | valid_percent |
|---|---|---|---|
| Absent | 147 | 58.8% | 59.0% |
| Present | 102 | 40.8% | 41.0% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(Grade_Level) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| Grade_Level | n | percent | valid_percent |
|---|---|---|---|
| high | 91 | 36.4% | 36.5% |
| low | 75 | 30.0% | 30.1% |
| moderate | 83 | 33.2% | 33.3% |
| NA | 1 | 0.4% | - |
mydata %>% janitor::tabyl(DeathTime) %>% janitor::adorn_pct_formatting(rounding = "half up",
digits = 1) %>% knitr::kable()| DeathTime | n | percent |
|---|---|---|
| MoreThan1Year | 101 | 40.4% |
| Within1Year | 149 | 59.6% |
race_stats <- summarytools::freq(mydata$Race)
print(race_stats, report.nas = FALSE, totals = FALSE, display.type = FALSE, Variable.label = "Race Group")variable = PreinvasiveComponent
type = character
na = 1 of 250 (0.4%)
unique = 3
Absent = 205 (82%)
Present = 44 (17.6%)
NA = 1 (0.4%)
## Frequency or custom tables for categorical variables
SmartEDA::ExpCTable(mydata, Target = NULL, margin = 1, clim = 10, nlim = 5, round = 2,
bin = NULL, per = T) Variable Valid Frequency Percent CumPercent
1 Sex Female 122 48.8 48.8
2 Sex Male 127 50.8 99.6
3 Sex NA 1 0.4 100.0
4 Sex TOTAL 250 NA NA
5 Race Asian 11 4.4 4.4
6 Race Bi-Racial 5 2.0 6.4
7 Race Black 30 12.0 18.4
8 Race Hispanic 49 19.6 38.0
9 Race NA 1 0.4 38.4
10 Race Native 1 0.4 38.8
11 Race Other 2 0.8 39.6
12 Race White 151 60.4 100.0
13 Race TOTAL 250 NA NA
14 PreinvasiveComponent Absent 205 82.0 82.0
15 PreinvasiveComponent NA 1 0.4 82.4
16 PreinvasiveComponent Present 44 17.6 100.0
17 PreinvasiveComponent TOTAL 250 NA NA
18 LVI Absent 148 59.2 59.2
19 LVI Present 102 40.8 100.0
20 LVI TOTAL 250 NA NA
21 PNI Absent 181 72.4 72.4
22 PNI NA 1 0.4 72.8
23 PNI Present 68 27.2 100.0
24 PNI TOTAL 250 NA NA
25 Group Control 119 47.6 47.6
26 Group NA 1 0.4 48.0
27 Group Treatment 130 52.0 100.0
28 Group TOTAL 250 NA NA
29 Grade 1 68 27.2 27.2
30 Grade 2 84 33.6 60.8
31 Grade 3 97 38.8 99.6
32 Grade NA 1 0.4 100.0
33 Grade TOTAL 250 NA NA
34 TStage 1 23 9.2 9.2
35 TStage 2 49 19.6 28.8
36 TStage 3 65 26.0 54.8
37 TStage 4 113 45.2 100.0
38 TStage TOTAL 250 NA NA
39 LymphNodeMetastasis Absent 147 58.8 58.8
40 LymphNodeMetastasis NA 1 0.4 59.2
41 LymphNodeMetastasis Present 102 40.8 100.0
42 LymphNodeMetastasis TOTAL 250 NA NA
43 Grade_Level high 91 36.4 36.4
44 Grade_Level low 75 30.0 66.4
45 Grade_Level moderate 83 33.2 99.6
46 Grade_Level NA 1 0.4 100.0
47 Grade_Level TOTAL 250 NA NA
48 DeathTime MoreThan1Year 101 40.4 40.4
49 DeathTime Within1Year 149 59.6 100.0
50 DeathTime TOTAL 250 NA NA
51 AntiX_intensity 1 25 10.0 10.0
52 AntiX_intensity 2 103 41.2 51.2
53 AntiX_intensity 3 121 48.4 99.6
54 AntiX_intensity NA 1 0.4 100.0
55 AntiX_intensity TOTAL 250 NA NA
56 AntiY_intensity 1 82 32.8 32.8
57 AntiY_intensity 2 108 43.2 76.0
58 AntiY_intensity 3 59 23.6 99.6
59 AntiY_intensity NA 1 0.4 100.0
60 AntiY_intensity TOTAL 250 NA NA
# A tibble: 16 x 5
col_name cnt common common_pcnt levels
<chr> <int> <chr> <dbl> <named list>
1 Death 3 TRUE 67.6 <tibble [3 × 3]>
2 DeathTime 2 Within1Year 59.6 <tibble [2 × 3]>
3 Grade 4 3 38.8 <tibble [4 × 3]>
4 Grade_Level 4 high 36.4 <tibble [4 × 3]>
5 Group 3 Treatment 52 <tibble [3 × 3]>
6 ID 250 001 0.4 <tibble [250 × 3]>
7 LVI 2 Absent 59.2 <tibble [2 × 3]>
8 LymphNodeMetastasis 3 Absent 58.8 <tibble [3 × 3]>
9 Name 250 Aaleyah 0.4 <tibble [250 × 3]>
10 PNI 3 Absent 72.4 <tibble [3 × 3]>
11 PreinvasiveComponent 3 Absent 82 <tibble [3 × 3]>
12 Race 8 White 60.4 <tibble [8 × 3]>
13 Sex 3 Male 50.8 <tibble [3 × 3]>
14 Smoker 3 FALSE 52.8 <tibble [3 × 3]>
15 TStage 4 4 45.2 <tibble [4 × 3]>
16 Valid 3 FALSE 53.6 <tibble [3 × 3]>
# A tibble: 3 x 3
value prop cnt
<chr> <dbl> <int>
1 Treatment 0.52 130
2 Control 0.476 119
3 <NA> 0.004 1
summarytools::stby(list(x = mydata$LVI, y = mydata$LymphNodeMetastasis), mydata$PNI,
summarytools::ctable)mydata %>% dplyr::select(characterVariables) %>% dplyr::select(PreinvasiveComponent,
PNI, LVI) %>% reactable::reactable(data = ., groupBy = c("PreinvasiveComponent",
"PNI"), columns = list(LVI = reactable::colDef(aggregate = "count")))Descriptive Statistics Age
mydata %>% jmv::descriptives(data = ., vars = "Age", hist = TRUE, dens = TRUE, box = TRUE,
violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE, skew = TRUE,
kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
─────────────────────────────────
Age
─────────────────────────────────
N 249
Missing 1
Mean 48.6
Median 48.0
Mode 48.0
Standard deviation 14.1
Variance 199
Minimum 25.0
Maximum 73.0
Skewness 0.0724
Std. error skewness 0.154
Kurtosis -1.17
Std. error kurtosis 0.307
25th percentile 36.0
50th percentile 48.0
75th percentile 61.0
─────────────────────────────────
Descriptive Statistics AntiX_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiX_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiX_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 2.39
Median 2.00
Mode 3.00
Standard deviation 0.663
Variance 0.439
Minimum 1.00
Maximum 3.00
Skewness -0.618
Std. error skewness 0.154
Kurtosis -0.648
Std. error kurtosis 0.307
25th percentile 2.00
50th percentile 2.00
75th percentile 3.00
──────────────────────────────────────────
Descriptive Statistics AntiY_intensity
mydata %>% jmv::descriptives(data = ., vars = "AntiY_intensity", hist = TRUE, dens = TRUE,
box = TRUE, violin = TRUE, dot = TRUE, mode = TRUE, sd = TRUE, variance = TRUE,
skew = TRUE, kurt = TRUE, quart = TRUE)
DESCRIPTIVES
Descriptives
──────────────────────────────────────────
AntiY_intensity
──────────────────────────────────────────
N 249
Missing 1
Mean 1.91
Median 2.00
Mode 2.00
Standard deviation 0.748
Variance 0.560
Minimum 1.00
Maximum 3.00
Skewness 0.152
Std. error skewness 0.154
Kurtosis -1.20
Std. error kurtosis 0.307
25th percentile 1.00
50th percentile 2.00
75th percentile 2.00
──────────────────────────────────────────
Overall
n 250
Age (mean (SD)) 48.59 (14.12)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 1.91 (0.75)
Overall
n 250
Age (mean (SD)) 48.59 (14.12)
AntiX_intensity (mean (SD)) 2.39 (0.66)
AntiY_intensity (mean (SD)) 1.91 (0.75)
variable = Age
type = double
na = 1 of 250 (0.4%)
unique = 50
min|max = 25 | 73
q05|q95 = 27.4 | 71
q25|q75 = 36 | 61
median = 48
mean = 48.58635
mydata %>% dplyr::select(continiousVariables) %>% SmartEDA::ExpNumStat(data = .,
by = "A", gp = NULL, Qnt = seq(0, 1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2)# A tibble: 3 x 10
col_name min q1 median mean q3 max sd pcnt_na hist
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <named list>
1 Age 25 36 48 48.6 61 73 14.1 0.4 <tibble [12…
2 AntiX_intens… 1 2 2 2.39 3 3 0.663 0.4 <tibble [12…
3 AntiY_intens… 1 1 2 1.91 2 3 0.748 0.4 <tibble [12…
# A tibble: 27 x 2
value prop
<chr> <dbl>
1 [-Inf, 24) 0
2 [24, 26) 0.0241
3 [26, 28) 0.0281
4 [28, 30) 0.0482
5 [30, 32) 0.0402
6 [32, 34) 0.0562
7 [34, 36) 0.0402
8 [36, 38) 0.0402
9 [38, 40) 0.0321
10 [40, 42) 0.0402
# … with 17 more rows
summarytools::stby(data = mydata, INDICES = mydata$PreinvasiveComponent, FUN = summarytools::descr,
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)with(mydata, summarytools::stby(Age, PreinvasiveComponent, summarytools::descr),
stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)## Summary statistics by – category
SmartEDA::ExpNumStat(mydata, by = "GA", gp = "PreinvasiveComponent", Qnt = seq(0,
1, 0.1), MesofShape = 2, Outlier = TRUE, round = 2) Vname Group TN nNeg nZero nPos NegInf PosInf NA_Value
1 Age PreinvasiveComponent:All 250 0 0 249 0 0 1
2 Age PreinvasiveComponent:Present 44 0 0 44 0 0 0
3 Age PreinvasiveComponent:Absent 205 0 0 204 0 0 1
4 Age PreinvasiveComponent:NA 0 0 0 0 0 0 0
Per_of_Missing sum min max mean median SD CV IQR Skewness Kurtosis
1 0.40 12098 25 73 48.59 48.0 14.12 0.29 25.00 0.07 -1.17
2 0.00 2144 25 73 48.73 48.5 15.11 0.31 27.75 -0.05 -1.26
3 0.49 9910 25 73 48.58 48.0 13.96 0.29 25.00 0.10 -1.16
4 NaN 0 Inf -Inf NaN NA NA NA NA NaN NaN
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% LB.25% UB.75% nOutliers
1 25 29.8 34.0 39.0 44 48.0 53.0 58.0 64 68.2 73 -1.50 98.50 0
2 25 29.0 30.6 40.0 45 48.5 54.0 58.3 64 69.4 73 -5.38 105.62 0
3 25 31.0 34.0 38.9 44 48.0 52.8 58.0 63 68.0 73 -1.50 98.50 0
4 NA NA NA NA NA NA NA NA NA NA NA NA NA 0
Codes for cross tables.24
dependent <- c("dependent1", "dependent2")
explanatory <- c("explanatory1", "explanatory2")
dependent <- "PreinvasiveComponent"
explanatory <- c("Sex", "Age", "Grade", "TStage")Change column = TRUE argument to get row or column percentages.
Cross Table PreinvasiveComponent
mydata %>%
summary_factorlist(dependent = 'PreinvasiveComponent',
explanatory = explanatory,
# column = TRUE,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE,
na_include=FALSE
# catTest = catTestfisher
) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))| Dependent: PreinvasiveComponent | Absent | Present | Total | p | |
|---|---|---|---|---|---|
| Sex | Female | 101 (49.5) | 20 (45.5) | 121 (48.8) | 0.625 |
| Male | 103 (50.5) | 24 (54.5) | 127 (51.2) | ||
| Age | Mean (SD) | 48.6 (14.0) | 48.7 (15.1) | 48.6 (14.1) | 0.997 |
| Grade | 1 | 53 (25.9) | 15 (34.9) | 68 (27.4) | 0.453 |
| 2 | 71 (34.6) | 12 (27.9) | 83 (33.5) | ||
| 3 | 81 (39.5) | 16 (37.2) | 97 (39.1) | ||
| TStage | 1 | 18 (8.8) | 5 (11.4) | 23 (9.2) | 0.934 |
| 2 | 41 (20.0) | 8 (18.2) | 49 (19.7) | ||
| 3 | 52 (25.4) | 12 (27.3) | 64 (25.7) | ||
| 4 | 94 (45.9) | 19 (43.2) | 113 (45.4) |
Codes for Survival Analysis25
https://link.springer.com/article/10.1007/s00701-019-04096-9
Calculate survival time
mydata$int <- lubridate::interval(lubridate::ymd(mydata$SurgeryDate), lubridate::ymd(mydata$LastFollowUpDate))
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)recode death status outcome as numbers for survival analysis
## Recoding mydata$Death into mydata$Outcome
mydata$Outcome <- forcats::fct_recode(as.character(mydata$Death), `1` = "TRUE", `0` = "FALSE")
mydata$Outcome <- as.numeric(as.character(mydata$Outcome))it is always a good practice to double-check after recoding26
0 1
FALSE 80 0
TRUE 0 169
library(survival)
# data(lung) km <- with(lung, Surv(time, status))
km <- with(mydata, Surv(OverallTime, Outcome))
head(km, 80) [1] 10.5 9.1 7.1+ 8.0 5.8 8.2+ 8.8+ 9.3 9.0 10.5+ 3.4+ 8.7
[13] 3.8 5.6 8.1 8.9 8.1 8.9 7.7+ 5.2+ 10.5 4.0 7.2 5.2+
[25] 4.2 3.3 4.0 4.0+ 11.0 10.7+ 7.3+ 11.4+ 6.4 8.7 NA+ 6.9+
[37] 5.9 10.5+ 8.8 8.2 11.5+ 7.0 6.4 9.6 5.6+ 9.9+ 8.2+ 9.1
[49] 3.3 11.6 7.3+ 10.8 7.6 11.5 4.0+ 8.1+ 10.4+ 6.2+ 9.6 6.5
[61] 11.8+ 4.1 6.6 5.8+ 9.3+ 5.4 7.1 9.6 3.9 4.9 3.3 9.0
[73] 5.8 9.3 5.2 8.3 10.3 10.6 7.1+ 6.4
Kaplan-Meier Plot Log-Rank Test
# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "LVI"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)# Drawing Survival Curves Using ggplot2
# https://rpkgs.datanovia.com/survminer/reference/ggsurvplot.html
mydata %>%
finalfit::surv_plot(.data = .,
dependent = "Surv(OverallTime, Outcome)",
explanatory = "LVI",
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)library(finalfit)
library(survival)
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit::finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni, row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))| Dependent: Surv(OverallTime, Outcome) | all | HR (univariable) | HR (multivariable) | |
|---|---|---|---|---|
| LVI | Absent | 148 (100.0) | - | - |
| Present | 102 (100.0) | 1.45 (1.06-1.98, p=0.021) | 1.45 (1.06-1.98, p=0.021) |
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names()
tUni_df_descr <- paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1],
" is ", tUni_df$x[2], ", there is ", tUni_df$hr_univariable[2], " times risk than ",
"when ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1],
".")When LVI is Present, there is 1.45 (1.06-1.98, p=0.021) times risk than when LVI is Absent.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 146 100 19.7 14.1 27.1
LVI=Present 101 69 10.5 9.3 12.8
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names() %>%
tibble::rownames_to_column()km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When {rowname}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::select(description) %>% dplyr::pull()When LVI=Absent, median survival is 19.7 [14.1 - 27.1, 95% CI] months., When LVI=Present, median survival is 10.5 [9.3 - 12.8, 95% CI] months.
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 71 52 0.612 0.0426 0.534 0.701
36 19 38 0.235 0.0424 0.165 0.334
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 52 0.391 0.0544 0.2978 0.514
36 5 15 0.120 0.0436 0.0593 0.245
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% dplyr::pull()When LVI=Absent, 12 month survival is 61% [53%-70.1%, 95% CI]., When LVI=Absent, 36 month survival is 23% [16%-33.4%, 95% CI]., When LVI=Present, 12 month survival is 39% [30%-51.4%, 95% CI]., When LVI=Present, 36 month survival is 12% [6%-24.5%, 95% CI].
Kaplan-Meier Plot Log-Rank Test
library(survival)
library(survminer)
library(finalfit)
mydata %>%
finalfit::surv_plot('Surv(OverallTime, Outcome)', 'LVI',
xlab='Time (months)', pval=TRUE, legend = 'none',
break.time.by = 12, xlim = c(0,60)
# legend.labs = c('a','b')
)Univariate Cox-Regression
explanatoryUni <- "LVI"
dependentUni <- "Surv(OverallTime, Outcome)"
tUni <- mydata %>% finalfit(dependentUni, explanatoryUni)
knitr::kable(tUni, row.names = FALSE, align = c("l", "l", "r", "r", "r", "r"))| Dependent: Surv(OverallTime, Outcome) | all | HR (univariable) | HR (multivariable) | |
|---|---|---|---|---|
| LVI | Absent | 148 (100.0) | - | - |
| Present | 102 (100.0) | 1.45 (1.06-1.98, p=0.021) | 1.45 (1.06-1.98, p=0.021) |
Univariate Cox-Regression Summary
tUni_df <- tibble::as_tibble(tUni, .name_repair = "minimal") %>% janitor::clean_names(dat = .,
case = "snake")
n_level <- dim(tUni_df)[1]
tUni_df_descr <- function(n) {
paste0("When ", tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[n +
1], ", there is ", tUni_df$hr_univariable[n + 1], " times risk than ", "when ",
tUni_df$dependent_surv_overall_time_outcome[1], " is ", tUni_df$x[1], ".")
}
results5 <- purrr::map(.x = c(2:n_level - 1), .f = tUni_df_descr)
print(unlist(results5))[1] "When LVI is Present, there is 1.45 (1.06-1.98, p=0.021) times risk than when LVI is Absent."
Median Survival
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 146 100 19.7 14.1 27.1
LVI=Present 101 69 10.5 9.3 12.8
km_fit_median_df <- summary(km_fit)
km_fit_median_df <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names(dat = .,
case = "snake") %>% tibble::rownames_to_column(.data = ., var = "LVI")
km_fit_median_definition <- km_fit_median_df %>% dplyr::mutate(description = glue::glue("When, LVI, {LVI}, median survival is {median} [{x0_95lcl} - {x0_95ucl}, 95% CI] months.")) %>%
dplyr::mutate(description = gsub(pattern = "thefactor=", replacement = " is ",
x = description)) %>% dplyr::select(description) %>% dplyr::pull()
km_fit_median_definitionWhen, LVI, LVI=Absent, median survival is 19.7 [14.1 - 27.1, 95% CI] months.
When, LVI, LVI=Present, median survival is 10.5 [9.3 - 12.8, 95% CI] months.
1-3-5-yr survival
Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
LVI=Absent
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 71 52 0.612 0.0426 0.534 0.701
36 19 38 0.235 0.0424 0.165 0.334
LVI=Present
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 52 0.391 0.0544 0.2978 0.514
36 5 15 0.120 0.0436 0.0593 0.245
km_fit_summary <- summary(km_fit, times = c(12, 36, 60))
km_fit_df <- as.data.frame(km_fit_summary[c("strata", "time", "n.risk", "n.event",
"surv", "std.err", "lower", "upper")])
km_fit_df strata time n.risk n.event surv std.err lower upper
1 LVI=Absent 12 71 52 0.6115055 0.04257648 0.53350053 0.7009157
2 LVI=Absent 36 19 38 0.2347686 0.04235846 0.16483932 0.3343638
3 LVI=Present 12 27 52 0.3911397 0.05442593 0.29777603 0.5137763
4 LVI=Present 36 5 15 0.1204428 0.04355881 0.05928425 0.2446936
km_fit_definition <- km_fit_df %>% dplyr::mutate(description = glue::glue("When {strata}, {time} month survival is {scales::percent(surv)} [{scales::percent(lower)}-{scales::percent(upper)}, 95% CI].")) %>%
dplyr::select(description) %>% dplyr::pull()
km_fit_definitionWhen LVI=Absent, 12 month survival is 61% [53%-70.1%, 95% CI].
When LVI=Absent, 36 month survival is 23% [16%-33.4%, 95% CI].
When LVI=Present, 12 month survival is 39% [30%-51.4%, 95% CI].
When LVI=Present, 36 month survival is 12% [6%-24.5%, 95% CI].
records n.max n.start events *rmean *se(rmean) median 0.95LCL
LVI=Absent 146 146 146 100 24.32960 1.653307 19.7 14.1
LVI=Present 101 101 101 69 18.16069 1.840202 10.5 9.3
0.95UCL
LVI=Absent 27.1
LVI=Present 12.8
km_fit_median_df <- summary(km_fit)
results1html <- as.data.frame(km_fit_median_df$table) %>% janitor::clean_names(dat = .,
case = "snake") %>% tibble::rownames_to_column(.data = ., var = "LVI")
results1html[, 1] <- gsub(pattern = "thefactor=", replacement = "", x = results1html[,
1])
knitr::kable(results1html, row.names = FALSE, align = c("l", rep("r", 9)), format = "html",
digits = 1)| LVI | records | n_max | n_start | events | rmean | se_rmean | median | x0_95lcl | x0_95ucl |
|---|---|---|---|---|---|---|---|---|---|
| LVI=Absent | 146 | 146 | 146 | 100 | 24.3 | 1.7 | 19.7 | 14.1 | 27.1 |
| LVI=Present | 101 | 101 | 101 | 69 | 18.2 | 1.8 | 10.5 | 9.3 | 12.8 |
Pairwise Comparisons
dependentKM <- "Surv(OverallTime, Outcome)"
explanatoryKM <- "TStage"
mydata %>%
finalfit::surv_plot(.data = .,
dependent = dependentKM,
explanatory = explanatoryKM,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60)
# legend.labs = c('a','b')
)Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 146 100 19.7 14.1 27.1
LVI=Present 101 69 10.5 9.3 12.8
print(km_fit,
scale=1,
digits = max(options()$digits - 4,3),
print.rmean=getOption("survfit.print.rmean"),
rmean = getOption('survfit.rmean'),
print.median=getOption("survfit.print.median"),
median = getOption('survfit.median')
)Call: survfit(formula = Surv(OverallTime, Outcome) ~ LVI, data = mydata)
3 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
LVI=Absent 146 100 19.7 14.1 27.1
LVI=Present 101 69 10.5 9.3 12.8
Interpret the results in context of the working hypothesis elaborated in the introduction and other relevant studies; include a discussion of limitations of the study.
Discuss potential clinical applications and implications for future research
Knijn, N., F. Simmer, and I. D. Nagtegaal. 2015. “Recommendations for Reporting Histopathology Studies: A Proposal.” Virchows Archiv 466 (6): 611–15. https://doi.org/10.1007/s00428-015-1762-3.
Schmidt, Robert L., Deborah J. Chute, Jorie M. Colbert-Getz, Adolfo Firpo-Betancourt, Daniel S. James, Julie K. Karp, Douglas C. Miller, et al. 2017. “Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty.” Archives of Pathology & Laboratory Medicine 141 (2): 279–87. https://doi.org/10.5858/arpa.2016-0200-OA.
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_01header.Rmd file for other general settings↩︎
Change echo = FALSE to hide codes after knitting.↩︎
See childRmd/_02fakeData.Rmd file for other codes↩︎
Synthea The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 19, 44 (2019) doi:10.1186/s12911-019-0793-0↩︎
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0793-0↩︎
https://medium.com/free-code-camp/how-our-test-data-generator-makes-fake-data-look-real-ace01c5bde4a↩︎
lung, cancer, breast datası ile birleştir↩︎
See childRmd/_03importData.Rmd file for other codes↩︎
See childRmd/_04briefSummary.Rmd file for other codes↩︎
Kişisel verilerin kaydedilmesi ve kişisel verileri hukuka aykırı olarak verme veya ele geçirme Türk Ceza Kanunu’nun 135. ve 136. maddesi kapsamında bizim hukuk sistemimizde suç olarak tanımlanmıştır. Kişisel verilerin kaydedilmesi suçunun cezası 1 ila 3 yıl hapis cezasıdır. Suçun nitelikli hali ise, kamu görevlisi tarafından görevin verdiği yetkinin kötüye kullanılarak veya belirli bir meslek veya sanatın sağladığı kolaylıktan yararlanılarak işlenmesidir ki bu durumda suçun cezası 1.5 ile 4.5 yıl hapis cezası olacaktır.↩︎
See childRmd/_06variableTypes.Rmd file for other codes↩︎
See childRmd/_07overView.Rmd file for other codes↩︎
Statistical Literacy Among Academic Pathologists: A Survey Study to Gauge Knowledge of Frequently Used Statistical Tests Among Trainees and Faculty. Archives of Pathology & Laboratory Medicine: February 2017, Vol. 141, No. 2, pp. 279-287. https://doi.org/10.5858/arpa.2016-0200-OA↩︎
From Table 1: Proposed items for reporting histopathology studies. Recommendations for reporting histopathology studies: a proposal Virchows Arch (2015) 466:611–615 DOI 10.1007/s00428-015-1762-3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460276/↩︎
See childRmd/_11descriptives.Rmd file for other codes↩︎
See childRmd/_12crossTables.Rmd file for other codes↩︎
See childRmd/_18survival.Rmd file for other codes, and childRmd/_19shinySurvival.Rmd for shiny application↩︎
JAMA retraction after miscoding – new Finalfit function to check recoding↩︎
See childRmd/_23footer.Rmd file for other codes↩︎
Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group. (2016) Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86 https://www.force11.org/software-citation-principles↩︎
A work by Serdar Balci
drserdarbalci@gmail.com